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Fecha
2026-04-16
Derechos de acceso
info:eu-repo/semantics/openAccess
Título de la revista
ISSN de la revista
Título del volumen
Editorial
Springer
Citas
Resumen
Detecting outliers in functional data analysis is challenging because curves can stray from the majority in many different ways. The Modified Epigraph Index (MEI) and Modified Hypograph Index (MHI) rank functions by the fraction of the domain on which one curve lies above or below another. While effective for spotting shape anomalies, their construction limits their ability to flag magnitude outliers. This paper introduces two new metrics, the Area-Based Epigraph Index (ABEI) and Area-Based Hypograph Index (ABHI) that quantify the area between curves, enabling simultaneous sensitivity to both magnitude and shape deviations. Building on these indices, we present EHyOut, a robust procedure that recasts functional outlier detection as a multivariate problem: for every curve, and for its first and second derivatives, we compute ABEI and ABHI and then apply multivariate outlier-detection techniques to the resulting feature vectors. Extensive simulations show that EHyOut remains stable across a wide range of contamination settings and often outperforms established benchmark methods. Moreover, applications to Spanish weather data and United Nations world population data further illustrate the practical utility and meaningfulness of this methodology.
Descripción
The registered version of this article, first published in “Computational Statistics 41, 72 (2026)", is available online at the publisher's website: Springer, https://doi.org/10.1007/s00180-026-01731-9
La versión registrada de este artículo, publicado por primera vez en “Computational Statistics 41, 72 (2026)", está disponible en línea en el sitio web del editor: Springer, https://doi.org/10.1007/s00180-026-01731-9
La versión registrada de este artículo, publicado por primera vez en “Computational Statistics 41, 72 (2026)", está disponible en línea en el sitio web del editor: Springer, https://doi.org/10.1007/s00180-026-01731-9
Categorías UNESCO
Palabras clave
epigraph, hypograph, outliers, functional data
Citación
Pulido, B., Franco-Pereira, A.M., Lillo, R.E. et al. Area-based epigraph and hypograph indices for functional outlier detection. Comput Stat 41, 72 (2026). https://doi.org/10.1007/s00180-026-01731-9
Centro
Facultad de Ciencias
Departamento
Estadística, Investigación Operativa y Cálculo Numérico